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## ===================================================
# docker-compose.yml
## ===================================================
# 1. 请在以下方案中选择任意一种，然后删除其他的方案
# 2. 修改你选择的方案中的environment环境变量，详情请见github wiki或者config.py
# 3. 选择一种暴露服务端口的方法，并对相应的配置做出修改：
# 【方法1: 适用于Linux，很方便，可惜windows不支持】与宿主的网络融合为一体，这个是默认配置
# network_mode: "host"
# 【方法2: 适用于所有系统包括Windows和MacOS】端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
# ports:
# - "12345:12345" # 注意！12345必须与WEB_PORT环境变量相互对应
# 4. 最后`docker-compose up`运行
# 5. 如果希望使用显卡，请关注 LOCAL_MODEL_DEVICE 和 英伟达显卡运行时 选项
## ===================================================
# 1. Please choose one of the following options and delete the others.
# 2. Modify the environment variables in the selected option, see GitHub wiki or config.py for more details.
# 3. Choose a method to expose the server port and make the corresponding configuration changes:
# [Method 1: Suitable for Linux, convenient, but not supported for Windows] Fusion with the host network, this is the default configuration
# network_mode: "host"
# [Method 2: Suitable for all systems including Windows and MacOS] Port mapping, mapping the container port to the host port (note that you need to delete network_mode: "host" first, and then add the following content)
# ports:
# - "12345: 12345" # Note! 12345 must correspond to the WEB_PORT environment variable.
# 4. Finally, run `docker-compose up`.
# 5. If you want to use a graphics card, pay attention to the LOCAL_MODEL_DEVICE and Nvidia GPU runtime options.
## ===================================================
## ===================================================
## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
## ===================================================
version: '3'
services:
gpt_academic_full_capability:
image: ghcr.io/binary-husky/gpt_academic_with_all_capacity:master
environment:
# 请查阅 `config.py`或者 github wiki 以查看所有的配置信息
API_KEY: ' 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 '
# USE_PROXY: ' True '
# proxies: ' { "http": "http://localhost:10881", "https": "http://localhost:10881", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4", "qianfan", "sparkv2", "spark", "chatglm"] '
BAIDU_CLOUD_API_KEY : ' bTUtwEAveBrQipEowUvDwYWq '
BAIDU_CLOUD_SECRET_KEY : ' jqXtLvXiVw6UNdjliATTS61rllG8Iuni '
XFYUN_APPID: ' 53a8d816 '
XFYUN_API_SECRET: ' MjMxNDQ4NDE4MzM0OSNlNjQ2NTlhMTkx '
XFYUN_API_KEY: ' 95ccdec285364869d17b33e75ee96447 '
ENABLE_AUDIO: ' False '
DEFAULT_WORKER_NUM: ' 20 '
WEB_PORT: ' 12345 '
ADD_WAIFU: ' False '
ALIYUN_APPKEY: ' RxPlZrM88DnAFkZK '
THEME: ' Chuanhu-Small-and-Beautiful '
ALIYUN_ACCESSKEY: ' LTAI5t6BrFUzxRXVGUWnekh1 '
ALIYUN_SECRET: ' eHmI20SVWIwQZxCiTD2bGQVspP9i68 '
# LOCAL_MODEL_DEVICE: ' cuda '
# 加载英伟达显卡运行时
# runtime: nvidia
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 1
# capabilities: [gpu]
# 【WEB_PORT暴露方法1: 适用于Linux】与宿主的网络融合
network_mode: "host"
# 【WEB_PORT暴露方法2: 适用于所有系统】端口映射
# ports:
# - "12345:12345" # 12345必须与WEB_PORT相互对应
# 启动容器后，运行main.py主程序
command: >
bash -c "python3 -u main.py"
## ===================================================
## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
## ===================================================
version: '3'
services:
gpt_academic_nolocalllms:
image: ghcr.io/binary-husky/gpt_academic_nolocal:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal)
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "sparkv2", "qianfan"] '
WEB_PORT: ' 22303 '
ADD_WAIFU: ' True '
# THEME: ' Chuanhu-Small-and-Beautiful '
# DEFAULT_WORKER_NUM: ' 10 '
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
# 与宿主的网络融合
network_mode: "host"
# 不使用代理网络拉取最新代码
command: >
bash -c "python3 -u main.py"
### ===================================================
### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
### ===================================================
version: '3'
services:
gpt_academic_with_chatglm:
image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master # (Auto Built by Dockerfile: docs/Dockerfile+ChatGLM)
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["chatglm", "qwen", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"] '
LOCAL_MODEL_DEVICE: ' cuda '
DEFAULT_WORKER_NUM: ' 10 '
WEB_PORT: ' 12303 '
ADD_WAIFU: ' True '
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
# 显卡的使用，nvidia0指第0个GPU
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
# 与宿主的网络融合
network_mode: "host"
command: >
bash -c "python3 -u main.py"
# P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
# command: >
# bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
### ===================================================
### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
### ===================================================
version: '3'
services:
gpt_academic_with_rwkv:
image: ghcr.io/binary-husky/gpt_academic_jittorllms:master
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "newbing", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"] '
LOCAL_MODEL_DEVICE: ' cuda '
DEFAULT_WORKER_NUM: ' 10 '
WEB_PORT: ' 12305 '
ADD_WAIFU: ' True '
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
# 显卡的使用，nvidia0指第0个GPU
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
# 与宿主的网络融合
network_mode: "host"
# 不使用代理网络拉取最新代码
command: >
python3 -u main.py
## ===================================================
## 【方案四】 ChatGPT + Latex
## ===================================================
version: '3'
services:
gpt_academic_with_latex:
image: ghcr.io/binary-husky/gpt_academic_with_latex:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal+Latex)
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
USE_PROXY: ' True '
proxies: ' { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] '
LOCAL_MODEL_DEVICE: ' cuda '
DEFAULT_WORKER_NUM: ' 10 '
WEB_PORT: ' 12303 '
# 与宿主的网络融合
network_mode: "host"
# 不使用代理网络拉取最新代码
command: >
bash -c "python3 -u main.py"
## ===================================================
## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
## ===================================================
version: '3'
services:
gpt_academic_with_audio:
image: ghcr.io/binary-husky/gpt_academic_audio_assistant:master
environment:
# 请查阅 `config.py` 以查看所有的配置信息
API_KEY: ' fk195831-IdP0Pb3W6DCMUIbQwVX6MsSiyxwqybyS '
USE_PROXY: ' False '
proxies: ' None '
LLM_MODEL: ' gpt-3.5-turbo '
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] '
ENABLE_AUDIO: ' True '
LOCAL_MODEL_DEVICE: ' cuda '
DEFAULT_WORKER_NUM: ' 20 '
WEB_PORT: ' 12343 '
ADD_WAIFU: ' True '
THEME: ' Chuanhu-Small-and-Beautiful '
ALIYUN_APPKEY: ' RoP1ZrM84DnAFkZK '
ALIYUN_TOKEN: ' f37f30e0f9934c34a992f6f64f7eba4f '
# (无需填写) ALIYUN_ACCESSKEY: ' LTAI5q6BrFUzoRXVGUWnekh1 '
# (无需填写) ALIYUN_SECRET: ' eHmI20AVWIaQZ0CiTD2bGQVsaP9i68 '
# 与宿主的网络融合
network_mode: "host"
# 不使用代理网络拉取最新代码
command: >
bash -c "python3 -u main.py"
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